Valence does not affect recognition.
Bibliographic record
Abstract
Valence refers to the extent to which a stimulus is viewed as negative or positive. One recent model of valence, the NEVER model (Bowen et al., 2018), predicts that in general negative words will be better remembered than positive or neutral words. However, this prediction is difficult to validate for recognition tests because the literature reports inconsistent findings. Three experiments reexamined whether valence affects recognition of words by taking advantage of the recent increase in the number of high-quality norms and databases, which allow for the construct ion of three sets of stimuli that differ in valence, but are equated on numerous other dimensions known to affect memory. Experiment 1 found no difference in recognition performance between positive and negative words; Experiment 2 found no difference between positive and neutral words; and Experiment 3 found no difference between neutral and negative words. The results disconfirm a prediction of the NEVER model and suggest that previous demonstrations of an effect of valence are due to confounding other dimensions with valence. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".